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This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (3698) Neurology and Neurosciences (384) Natural Language Processing (1279) Decision Support for Health Professionals (2228) Machine Learning (3167) Hypertension Prevention and Treatment (347) Artificial Intelligence (4758)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66055, first published .
Doctor's hand on laptop keyboard in clinic, medical technology

Knowledge Graph–Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation

Knowledge Graph–Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation

Authors of this article:

Yulong Xia1 Author Orcid Image ;   Jie Li2 Author Orcid Image ;   Bo Deng1 Author Orcid Image ;   Qilin Huang2 Author Orcid Image ;   Fenglin Cai2 Author Orcid Image ;   Yanfeng Xie1 Author Orcid Image ;   Xiaochuan Sun1 Author Orcid Image ;   Quanhong Shi1 Author Orcid Image ;   Wei Dan1 Author Orcid Image ;   Yan Zhan1 Author Orcid Image ;   Li Jiang1 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Gao Q, Jin Y, Sun Y, Jin M, Tang L, Chen Y, She Y, Li M. Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions. Diagnostics 2026;16(5):752 View
  2. de Oliveira Manduca Palmiero H, Gilberto Carlotti C, Gadelha Figueiredo E. AI Models for Surgical Decision Support in Spontaneous Intracerebral Hemorrhage: A Systematic Review in Relation to Trials and Guidelines. Neurocritical Care 2026;44(3):1063 View
  3. Zgheib R, Naggar A, Kolankeh A, Takshe A. Toward Trustworthy AI for Autism Spectrum Disorder: A Systematic Review of Multimodal Systems, Knowledge Representation, and Clinical Integration. Information 2026;17(8):802 View

Conference Proceedings

  1. España-Lopera J, Núñez-Valdez E, García-Díaz V. 2026 2nd International Conference on Federated Learning and Intelligent Computing Systems (FLICS). A Technical Taxonomy and Evidence-Based Framework for Assessing Clinical Large Language Model Systems View